Unruptured intracranial aneurysm growth trajectory: occurrence and rate of enlargement in 520 longitudinally followed cases

Cigarette Smoking and Observed Growth of Unruptured Intracranial Aneurysms: A Systematic Literature Review and Meta-Analysis

BACKGROUND

Smoking and observed growth of intracranial aneurysms are known risk factors for rupture. The mechanism by which smoking increases this risk is not completely elucidated. Furthermore, an association between smoking and aneurysm growth has not been clearly defined in the literature. We hypothesize that smoking is associated with aneurysm growth, which, in turn, may serve as one of the mechanisms by which smoking drives rupture risk.

METHODS

We report a systematic review of the literature in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Using the R software, we performed a meta-analysis to investigate the association between smoking and the growth of unruptured intracranial aneurysms. Studies on familial aneurysms and genetic syndromes known to increase the risk of aneurysms were excluded.

RESULTS

Eighteen observational studies were included with a total of 3535 patients and 4289 aneurysms with a mean follow-up period ranging from 17 to 226 months. The mean age among the studies ranged from 38.4 to 73.9 years; 74% of patients were female. Ever-smoking status (odds ratio, 1.10 [95% CI, 0.87-1.38]) and current smoking status (odds ratio, 1.43 [95% CI, 0.84-2.43]) did not show a statistically significant association with growth of intracranial aneurysms. Patients currently smoking did not have a statistically significant association with the growth of intracranial aneurysms (odds ratio, 1.18 [95% CI, 0.72-1.93]) compared with patients without a smoking history. No significant association was found in patients who previously smoked compared with patients who never smoked (odds ratio, 1.46 [95% CI, 0.88-2.43]).

CONCLUSIONS

Smoking is not clearly associated with the growth of unruptured intracranial aneurysms, despite trends being observed, there is no statistical association. The mechanism by which smoking increases rupture risk might not be growth. In patients for whom observation is recommended, the absence of growth over time in the setting of smoking history does not, therefore, imply protection from rupture.

A Computational Pipeline to Investigate Longitudinal Blood Flow Changes in the Circle of Willis of Patients with Stable and Growing Aneurysms

Changes in cerebral blood flow are often associated with the initiation and development of different life-threatening medical conditions including aneurysm rupture and ischemic stroke. Nevertheless, it is not fully clear how haemodynamic changes in time across the Circle of Willis (CoW) are related with intracranial aneurysm (IA) growth. In this work, we introduced a novel reduced-order modelling strategy for the systematic quantification of longitudinal blood flow changes across the whole CoW in patients with stable and unstable/growing aneurysm. Magnetic Resonance Angiography (MRA) images were converted into one-dimensional (1-D) vessel networks through a semi-automated procedure, with a level of geometric reconstruction accuracy controlled by user-dependent parameters. The proposed pipeline was used to systematically analyse longitudinal haemodynamic changes in seven different clinical cases. Our preliminary simulation results indicate that growing aneurysms are not necessarily associated with significant changes in mean flow over time. A concise sensitivity analysis also shed light on which modelling aspects need to be further characterized to have reliable patient-specific predictions. This study poses the basis for investigating how time-dependent changes in the vasculature affect the haemodynamics across the whole CoW in patients with stable and growing aneurysms.

A Decision Tree Model to Help Treatment Decision-Making for Unruptured Intracranial Aneurysms: A Multi-center, Long-Term Follow-up Study in a Large Chinese Cohort

Chinese population have a high prevalence of unruptured intracranial aneurysm (UIA). Clinical and imaging risk factors predicting UIA growth or rupture are poorly understood in the Chinese population due to the lack of large-scale longitudinal studies, and the treatment decision for UIA patients was challenging. Develop a decision tree (DT) model for UIA instability, and validate its performance in multi-center studies. Single-UIA patients from two prospective, longitudinal multicenter cohort studies were analyzed, and set as the development cohort and validation cohort. The primary endpoint was UIA instability (rupture, growth, or morphological change). A DT was established within the development cohort and validated within the validation cohort. The performance of clinicians in identifying unstable UIAs before and after the help of the DT was compared using the area under curve (AUC). The development cohort included 1270 patients with 1270 UIAs and a follow-up duration of 47.2 ± 15.5 months. Aneurysm instability occurred in 187 (14.7%) patients. Multivariate Cox analysis revealed hypertension (hazard ratio [HR], 1.54; 95%CI, 1.14-2.09), aspect ratio (HR, 1.22; 95%CI, 1.17-1.28), size ratio (HR, 1.31; 95%CI, 1.23-1.41), bifurcation configuration (HR, 2.05; 95%CI, 1.52-2.78) and irregular shape (HR, 4.30; 95%CI, 3.19-5.80) as factors of instability. In the validation cohort (n = 106, 12 was unstable), the DT model incorporating these factors was highly predictive of UIA instability (AUC, 0.88 [95%CI, 0.79-0.97]), and superior to existing UIA risk scales such as PHASES and ELAPSS (AUC, 0.77 [95%CI, 0.67-0.86] and 0.76 [95%CI, 0.66-0.86], P < 0.001). Within all 1376 single-UIA patients, the use of the DT significantly improved the accuracy of junior neurosurgical clinicians to identify unstable UIAs (AUC from 0.63 to 0.82, P < 0.001). The DT incorporating hypertension, aspect ratio, size ratio, bifurcation configuration and irregular shape was able to predict UIA instability better than existing clinical scales in Chinese cohorts. CLINICAL TRIAL REGISTRATION: IARP-CP cohort were included (unique identifier: ChiCTR1900024547. Published July 15, 2019. Completed December 30, 2020), with 100-Project phase-I cohort (unique identifier: NCT04872842, Published May 5, 2021. Completed November 8, 2022) as the development cohort. The 100-Project phase-II cohort (unique identifier: NCT05608122. Published November 8, 2022) as the validation cohort.

Is Thyroid Dysfunction Associated with Unruptured Intracranial Aneurysms? A Population-Based, Nested Case-Control Study from Korea

Background: Few risk factors for the development of intracranial aneurysms (IAs) are known. We investigated the potential role of thyroid diseases in IA development using nationwide real-world data. Methods: A nested case-control study within the National Health Insurance Service-National Sample Cohort data from 2002 to 2019 was performed. A total of 5335 patients with unruptured IA were matched by age and sex with 80,025 controls at a ratio of 1:15. We estimated the odds ratios (ORs) and corresponding confidence intervals [CIs] between thyroid diseases and unruptured IA using a multivariable conditional logistic regression model. Results: Tobacco smoking, use of antihypertensive medication, and hypothyroidism were significantly associated with an elevated risk for unruptured IA in univariate analysis. In multivariable analysis, a history of hypothyroidism was associated with unruptured IA (adjusted OR: 1.46 [CI: 1.26-1.69]). Among patients with hypothyroidism, long-term use of thyroid hormone for >5 years was associated with a reduced risk for unruptured IA (adjusted OR: 0.69 [CI: 0.48-0.99]). A history of hyperthyroidism was associated with a reduced risk for unruptured IAs (adjusted OR: 0.71 [CI: 0.54-0.93]). In secondary analyses of the data according to sex, the respective observed associations between hypothyroidism and hyperthyroidism and the risk of IAs were found to be statistically significant in females but not in males. Conclusions: Hypothyroidism is associated with an increased risk of unruptured IAs, whereas hyperthyroidism is associated with a reduced risk. Overall, the findings suggest that thyroid hormones may play a protective role in the development of unruptured IAs. Further studies are needed to clarify potential direct causality and the biologic mechanisms relating thyroid dysfunction and unruptured IA.

Characteristics and evolution of cerebral aneurysms among adults living with HIV: A retrospective, longitudinal case series

OBJECTIVE

Previous research indicates an increased risk of cerebral aneurysm formation in adults living with human immunodeficiency virus (ALWH), however there are few longitudinal studies on the risk factors for and outcomes of cerebral aneurysms in this population. We aim to describe the characteristics and evolution of cerebral aneurysms in a large cohort of ALWH.

MATERIALS AND METHODS

A chart review was completed for all adults evaluated at an urban, safety-net U.S. hospital between January 1, 2000, and October 22, 2021, with history of both HIV and at least one cerebral aneurysm.

RESULTS

A total of 82 cerebral aneurysms were identified amongst 50 patients (52% female sex). Forty-six percent of patients with a nadir CD4 count less than 200 cells/mm³ (N=13) and 44% of patients with maximum viral load >10,000 copies/mL (N=18) developed new aneurysms or were found to have aneurysm growth over time compared with 29% of patients with a CD4 nadir above 200 cells/mm³ (N=21) and 22% of patients with maximum viral load </= 75 copies/mL (N=9). New aneurysms were found, or existing aneurysms grew in 67% of those not on antiretroviral therapy (ART) at time of aneurysm diagnosis (N=6), 38% of those with inconsistent ART use (N=8), and 21% of those with consistent ART (N=19).

CONCLUSIONS

Among ALWH, lower CD4 nadir, higher zenith viral load, and inconsistent ART use may contribute to aneurysm formation or growth. Further studies are needed to more thoroughly characterize the association between immunologic status and cerebral aneurysm formation.

The mechanism and therapy of aortic aneurysms

Aortic aneurysm is a chronic aortic disease affected by many factors. Although it is generally asymptomatic, it poses a significant threat to human life due to a high risk of rupture. Because of its strong concealment, it is difficult to diagnose the disease in the early stage. At present, there are no effective drugs for the treatment of aneurysms. Surgical intervention and endovascular treatment are the only therapies. Although current studies have discovered that inflammatory responses as well as the production and activation of various proteases promote aortic aneurysm, the specific mechanisms remain unclear. Researchers are further exploring the pathogenesis of aneurysms to find new targets for diagnosis and treatment. To better understand aortic aneurysm, this review elaborates on the discovery history of aortic aneurysm, main classification and clinical manifestations, related molecular mechanisms, clinical cohort studies and animal models, with the ultimate goal of providing insights into the treatment of this devastating disease. The underlying problem with aneurysm disease is weakening of the aortic wall, leading to progressive dilation. If not treated in time, the aortic aneurysm eventually ruptures. An aortic aneurysm is a local enlargement of an artery caused by a weakening of the aortic wall. The disease is usually asymptomatic but leads to high mortality due to the risk of artery rupture.

RNA Expression Signatures of Intracranial Aneurysm Growth Trajectory Identified in Circulating Whole Blood

After detection, identifying which intracranial aneurysms (IAs) will rupture is imperative. We hypothesized that RNA expression in circulating blood reflects IA growth rate as a surrogate of instability and rupture risk. To this end, we performed RNA sequencing on 66 blood samples from IA patients, for which we also calculated the predicted aneurysm trajectory (PAT), a metric quantifying an IA's future growth rate. We dichotomized dataset using the median PAT score into IAs that were either more stable and more likely to grow quickly. The dataset was then randomly divided into training (n = 46) and testing cohorts (n = 20). In training, differentially expressed protein-coding genes were identified as those with expression (TPM > 0.5) in at least 50% of the samples, a q-value < 0.05 (based on modified F-statistics with Benjamini-Hochberg correction), and an absolute fold-change ≥ 1.5. Ingenuity Pathway Analysis was used to construct networks of gene associations and to perform ontology term enrichment analysis. The MATLAB Classification Learner was then employed to assess modeling capability of the differentially expressed genes, using a 5-fold cross validation in training. Finally, the model was applied to the withheld, independent testing cohort (n = 20) to assess its predictive ability. In all, we examined transcriptomes of 66 IA patients, of which 33 IAs were "growing" (PAT ≥ 4.6) and 33 were more "stable". After dividing dataset into training and testing, we identified 39 genes in training as differentially expressed (11 with decreased expression in "growing" and 28 with increased expression). Model genes largely reflected organismal injury and abnormalities and cell to cell signaling and interaction. Preliminary modeling using a subspace discriminant ensemble model achieved a training AUC of 0.85 and a testing AUC of 0.86. In conclusion, transcriptomic expression in circulating blood indeed can distinguish "growing" and "stable" IA cases. The predictive model constructed from these differentially expressed genes could be used to assess IA stability and rupture potential.

Predicting the growth of middle cerebral artery bifurcation aneurysms using differences in the bifurcation angle and inflow coefficient

OBJECTIVE

Growing intracranial aneurysms (IAs) are prone to rupture. Previous cross-sectional studies using postrupture morphology have shown the morphological or hemodynamic features related to IA rupture. Yet, which morphological or hemodynamic differences of the prerupture status can predict the growth and rupture of smaller IAs remains unknown. The purpose of this longitudinal study was to investigate the effects of morphological features and the hemodynamic environment on the growth of IAs at middle cerebral artery (MCA) bifurcations during the follow-up period.

METHODS

One hundred two patients with MCA M1–2 bifurcation saccular IAs who underwent follow-up for more than 2 years at the authors’ institutions between 2011 and 2019 were retrospectively identified. During the follow-up period, cases involving growth of MCA IAs were assigned to the event group, and those with MCA IAs unchanged in size were assigned to the control group. The morphological parameters examined were aneurysmal neck length, dome height, aspect ratio and volume, M1 and M2 diameters and their ratio, and angle configurations among M1, M2, and the aneurysm. Hemodynamic parameters were flow rate and wall shear stress in M1, M2, and the aneurysm, including the aneurysmal inflow rate coefficient (AIRC), defined as the ratio of the aneurysmal inflow rate to the M1 flow rate. Those parameters were compared statistically between the two groups. Correlations between morphological and hemodynamic parameters were also examined.

RESULTS

Eighty-three of 102 patients were included: 25 with growing MCA IAs (event group) and 58 with stable MCA IAs (control group). The median patient age at initial diagnosis was 66.9 (IQR 59.8–72.3) years. The median follow-up period was 48.5 (IQR 36.5–65.6) months. Both patient age and the AIRC were significant independent predictors of the growth of MCA IAs. Moreover, the AIRC was strongly correlated with sharper bifurcation and inflow angles, as well as wider inclination angles between the M1 and M2 arteries.

CONCLUSIONS

The AIRC was a significant independent predictor of the growth of MCA IAs. Sharper bifurcation and inflow angles and wider inclination angles between the M1 and M2 arteries were correlated with the AIRC. MCA IAs with such a bifurcation configuration are more prone to grow and rupture.

Development of multiple intracranial aneurysms: beyond the common risk factors

OBJECTIVE

The prevalence of multiple intracranial aneurysms (MIAs) has increased over the last decades. Because MIAs have been identified as an independent risk factor for formation, growth, and rupture of intracranial aneurysms (IAs), a more profound understanding of the underlying pathophysiology of MIAs is needed. Therefore, the authors' extensive institutional aneurysm database was analyzed to elucidate differences between patients with a single IA (SIA) and those with MIAs.

METHODS

A total of 2446 patients seen with or for IAs at the University Hospital of Essen, Essen, Germany, from January 2003 to June 2016 were included in this retrospective cohort study and were separated into MIA and SIA subgroups. Patient data were screened for sociodemographic and radiographic parameters, preexisting medical conditions, and results of blood examinations. These parameters were analyzed for their correlations with MIAs and absolute number of IAs.

RESULTS

MIAs were identified in 853 (34.9%) patients. In multivariable analysis, MIAs were independently associated with female sex (p = 0.001), arterial hypertension (p = 0.023), tobacco abuse (p = 0.009), AB blood group (p = 0.010), and increased admission values for C-reactive protein (p = 0.006), mean corpuscular volume (p = 0.009), and total serum protein (p = 0.034), but not with diagnostic modality (3D vs 2D digital subtraction angiography, p = 0.912). Absolute number of IAs was independently associated with female sex (p < 0.001), arterial hypertension (p = 0.014), familial predisposition to IA (p = 0.015), tobacco consumption (p = 0.025), increased mean corpuscular volume (p = 0.002), and high platelet count (p = 0.007).

CONCLUSIONS

In this sizable consecutive series of patients with IAs, the authors confirmed the impact of common IA risk factors on the genesis of MIAs. In addition, specific hemorheological and hemocytological features may also contribute to the development of MIAs.

PDE-constrained shape registration to characterize biological growth and morphogenesis from imaging data

We propose a PDE-constrained shape registration algorithm that captures the deformation and growth of biological tissue from imaging data. Shape registration is the process of evaluating optimum alignment between pairs of geometries through a spatial transformation function. We start from our previously reported work, which uses 3D tensor product B-spline basis functions to interpolate 3D space. Here, the movement of the B-spline control points, composed with an implicit function describing the shape of the tissue, yields the total deformation gradient field. The deformation gradient is then split into growth and elastic contributions. The growth tensor captures addition of mass, i.e. growth, and evolves according to a constitutive equation which is usually a function of the elastic deformation. Stress is generated in the material due to the elastic component of the deformation alone. The result of the registration is obtained by minimizing a total energy functional which includes: a distance measure reflecting similarity between the shapes, and the total elastic energy accounting for the growth of the tissue. We apply the proposed shape registration framework to study zebrafish embryo epiboly process and tissue expansion during skin reconstruction surgery. We anticipate that our PDE-constrained shape registration method will improve our understanding of biological and medical problems in which tissues undergo extreme deformations over time.

A Meta-Analysis of Rupture Risk for Intracranial Aneurysms 10 mm or Less in Size Selected for Conservative Management Without Repair

BACKGROUND

Small unruptured intracranial aneurysms (UIAs) are considered to have low risk of rupture. The proportion of UIAs measuring 10 mm or less in size that rupture when selected for conservative management without repair is not well known. The aim of this study is to determine the proportion of UIAs that rupture by size threshold from ≤10 to ≤3 mm when selected for management without repair and to determine the level of precision and sources of heterogeneity in the rupture risk estimate.

METHODS

This study was prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42019121522). The Ovid MEDLINE, EMBASE, Web of Science Core Collection, and the Cochrane Central Register of Controlled Trials were searched (inception to August 2020). Studies with longitudinal follow-up of patients with UIAs ( ≤10 mm to ≤3 mm) without endovascular or neurosurgical repair were eligible. We included studies, which provided details of aneurysm size and in which UIA rupture was reported as an outcome. The primary outcome of the pooled proportion of UIA rupture during follow-up was synthesized with random-effects meta-analysis; heterogeneity was explored using meta-regression.

RESULTS

A total of 31 studies that included 13,800 UIAs ≤10 mm in size were eligible for data synthesis. The pooled proportion of ≤10 mm UIAs that ruptured when managed without repair was 1.1% (95% CI 0.8-1.5; I 2 = 52.9%) over 3.7 years. Findings were consistent in sensitivity analyses at all the size stratified thresholds including ≤5 and ≤3 mm; rupture occurred in 1.0% (95% CI 0.8-1.3; I 2 = 0%) of 7,280 ≤5 mm UIAs and 0.8% (95% CI 0.4-1.5; I 2 = 0%) of 1,228 ≤3 mm UIAs managed without repair. In higher quality studies with lower risk of bias, rupture occurred in 1.8% (95% CI 1.5-2.0; I 2 = 0%) over 3.9 years. In meta-regression, aneurysm size, shape, anatomical location, and exposure to prior subarachnoid hemorrhage were not identified as sources of heterogeneity.

CONCLUSION

For every 1,000 UIAs that are 10 mm or less in size and selected for conservative management without repair, between 8 and 15 UIAs are estimated to rupture over 3.7 years. When stratified by size, these pooled rupture risk estimates are consistent and clinically applicable for ≤5 mm UIAs selected for management without repair.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/prospero/, identifier: CRD42019121522.

Hemodynamic Analysis of Pipeline Embolization Device Stent for Treatment of Giant Intracranial Aneurysm under Unsupervised Learning Algorithm

To treat large intracranial aneurysms, pipeline embolization device (PED) stent with unsupervised learning algorithms was utilized. Unsupervised learning model algorithm was used to screen aneurysm health big data, find aneurysm blood flow and PED stent positioning characteristic parameters, and guide PED stent treatment of intracranial aneurysms. The research objects were 100 patients with intracranial large aneurysm admitted to X Hospital of X Province from June 2020 to June 2021, who were enrolled into two groups. One group used the prototype transfer generative adversarial network (PTGAN) model to measure mean blood flow and mean vascular pressure and guide the placement of PED stents (PTGAN group). The other group did not use the model to place PED (control group). The PTGAN model can learn feature information from horizontal and vertical directions, with smooth edges and prominent features, which can effectively extract the main morphological and texture features of aneurysms. Compared with the convolutional neural network (CNN) model, the accuracy of the PTGAN model increased by 8.449% (87.452%-79.003%), and the precision increased by 8.347% (91.23%-82.883%). The recall rate increased by 7.011% (87.231%-80.22%), and the F1 score increased by 8.09% (89.73%-81.64%). After the adoption of the PTGAN model, the average blood flow inside the aneurysm body was 0.22 (m/s). After the adoption of the CNN model, the average blood flow inside the aneurysm body was 0.21 (m/s), and the difference was 0.01 (m/s), which was considerable (p < 0.05). Through this research, it was found that the PTGAN model was better than the CNN model in terms of accuracy, precision, recall, and F1 score values. The PTGAN model was better than the CNN model in detecting the average blood flow rate and average blood pressure after treatment, and the blood flowed smoothly. Postoperative complications and postoperative relief were also better than those of the control group. In summary, based on the unsupervised learning algorithm, the PED stent had a good adoption effect in the treatment of intracranial aneurysms and was suitable for subsequent treatment.

Risk of Rupture After Intracranial Aneurysm Growth

Importance

Unruptured intracranial aneurysms not undergoing preventive endovascular or neurosurgical treatment are often monitored radiologically to detect aneurysm growth, which is associated with an increase in risk of rupture. However, the absolute risk of aneurysm rupture after detection of growth remains unclear.

Objective

To determine the absolute risk of rupture of an aneurysm after detection of growth during follow-up and to develop a prediction model for rupture.

Design, Setting, and Participants

Individual patient data were obtained from 15 international cohorts. Patients 18 years and older who had follow-up imaging for at least 1 untreated unruptured intracranial aneurysm with growth detected at follow-up imaging and with 1 day or longer of follow-up after growth were included. Fusiform or arteriovenous malformation-related aneurysms were excluded. Of the 5166 eligible patients who had follow-up imaging for intracranial aneurysms, 4827 were excluded because no aneurysm growth was detected, and 27 were excluded because they had less than 1 day follow-up after detection of growth.

Exposures

All included aneurysms had growth, defined as 1 mm or greater increase in 1 direction at follow-up imaging.

Main Outcomes and Measures

The primary outcome was aneurysm rupture. The absolute risk of rupture was measured with the Kaplan-Meier estimate at 3 time points (6 months, 1 year, and 2 years) after initial growth. Cox proportional hazards regression was used to identify predictors of rupture after growth detection.

Results

A total of 312 patients were included (223 [71%] were women; mean [SD] age, 61 [12] years) with 329 aneurysms with growth. During 864 aneurysm-years of follow-up, 25 (7.6%) of these aneurysms ruptured. The absolute risk of rupture after growth was 2.9% (95% CI, 0.9-4.9) at 6 months, 4.3% (95% CI, 1.9-6.7) at 1 year, and 6.0% (95% CI, 2.9-9.1) at 2 years. In multivariable analyses, predictors of rupture were size (7 mm or larger hazard ratio, 3.1; 95% CI, 1.4-7.2), shape (irregular hazard ratio, 2.9; 95% CI, 1.3-6.5), and site (middle cerebral artery hazard ratio, 3.6; 95% CI, 0.8-16.3; anterior cerebral artery, posterior communicating artery, or posterior circulation hazard ratio, 2.8; 95% CI, 0.6-13.0). In the triple-S (size, site, shape) prediction model, the 1-year risk of rupture ranged from 2.1% to 10.6%.

Conclusion and Relevance

Within 1 year after growth detection, rupture occurred in approximately 1 of 25 aneurysms. The triple-S risk prediction model can be used to estimate absolute risk of rupture for the initial period after detection of growth.

Incidental intracranial saccular aneurysm in a patient with post-Covid-19 headache: What to do with the incidentaloma?

Case report

Development and validation of an institutional nomogram for aiding aneurysm rupture risk stratification

Rupture risk stratification is critical for incidentally detected intracranial aneurysms. Here we developed and validated an institutional nomogram to solve this issue. We reviewed the imaging and clinical databases for aneurysms from January 2015 to September 2018. Aneurysms were reconstructed and morphological features were extracted by the Pyradiomics in python. Multiple logistic regression was performed to develop the nomogram. The consistency of the nomogram predicted rupture risks and PHASES scores was assessed. The performance of the nomogram was evaluated by the discrimination, calibration, and decision curve analysis (DCA). 719 aneurysms were enrolled in this study. For each aneurysm, twelve morphological and nine clinical features were obtained. After logistic regression, seven features were enrolled in the nomogram, which were SurfaceVolumeRatio, Flatness, Age, Hyperlipemia, Smoker, Multiple aneurysms, and Location of the aneurysm. The nomogram had a positive and close correlation with PHASES score in predicting aneurysm rupture risks. AUCs of the nomogram in discriminating aneurysm rupture status was 0.837 in a separate testing set. The calibration curves fitted well and DCA demonstrated positive net benefits of the nomogram in guiding clinical decisions. In conclusion, Pyradiomics derived morphological features based institutional nomogram was useful for aneurysm rupture risk stratification.

Deep Shape Features for Predicting Future Intracranial Aneurysm Growth

Introduction: Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, which is often fatal. Aneurysm growth is considered a surrogate of rupture risk; therefore, the study aimed to develop and evaluate prediction models of future artificial intelligence (AI) growth based on baseline aneurysm morphology as a computer-aided treatment decision support.

Materials and methods: Follow-up CT angiography (CTA) and magnetic resonance angiography (MRA) angiograms of 39 patients with 44 IAs were classified by an expert as growing and stable (25/19). From the angiograms vascular surface meshes were extracted and the aneurysm shape was characterized by established morphologic features and novel deep shape features. The features corresponding to the baseline aneurysms were used to predict future aneurysm growth using univariate thresholding, multivariate random forest and multi-layer perceptron (MLP) learning, and deep shape learning based on the PointNet++ model.

Results: The proposed deep shape feature learning method achieved an accuracy of 0.82 (sensitivity = 0.96, specificity = 0.63), while the multivariate learning and univariate thresholding methods were inferior with an accuracy of up to 0.68 and 0.63, respectively.

Conclusion: High-performing classification of future growing IAs renders the proposed deep shape features learning approach as the key enabling tool to manage rupture risk in the “no treatment” paradigm of patient follow-up imaging.

A Volumetric Metric for Monitoring Intracranial Aneurysms: Repeatability and Growth Criteria in a Longitudinal MR Imaging Study

BACKGROUND AND PURPOSE

The reliability of contrast-enhanced MRA in monitoring serial volumetric changes of unruptured intracranial aneurysms has not been established. We aimed to determine the coefficient of variance of contrast-enhanced MRA in measuring aneurysm volumes, thus establishing criteria for aneurysm growth and permitting identification of variables predictive of growth.

MATERIALS AND METHODS

Aneurysm volumes were measured from serial contrast-enhanced MRA studies of patients with untreated intracranial aneurysms who underwent >2 sequential MR imaging evaluations. After coregistering all sequential studies in 3D space for each aneurysm and signal intensity normalization, aneurysm volume was determined across all time points. A linear mixed effects model was built to estimate the coefficient of variance of the measurement as well as to determine predictive variables. Growth was defined as relative growth exceeding 2 times the measurement coefficient of variance (sudden growth, as 4 times the coefficient of variance).

RESULTS

A total of 95 patients with 112 aneurysms were included (5.9 scans during 4.0 years on average, 616 scan measurements in total). The coefficient of variance was 5.5% of the aneurysm volume, and the relative growth rate was dependent on the location: anterior cerebral artery, 4.52% per year; vertebral artery, 2.46% per year; middle cerebral artery, 2.74% per year; basilar artery, 2.36% per year; internal carotid artery, 1.14% per year. Thirty-six of 112 (32%) aneurysms were characterized as growing, and 11/36 of them had an episode of sudden growth.

CONCLUSIONS

Volume measurement of unruptured intracranial aneurysms by contrast-enhanced MRA seems a reliable metric for tracking the growth trajectory of aneurysms. Furthermore, the aneurysm growth rate differs among different locations.

Management of unruptured incidentally found intracranial saccular aneurysms

Unruptured intracranial saccular aneurysms occur in 3-5% of the general population. As the use of diagnostic medical imaging has steadily increased over the past few decades with the increased availability of computed tomography (CT) and magnetic resonance imaging (MRI), so has the detection of incidental aneurysms. The management of an unruptured intracranial saccular aneurysm is challenging for both patients and physicians, as the decision to intervene must weigh the risk of rupture and resultant subarachnoid hemorrhage against the risk inherent to the surgical or endovascular procedure. The purpose of this paper is to provide an overview of factors to be considered in the decision to offer treatment for unruptured intracranial aneurysms in adults. In addition, we review aneurysm and patient characteristics that favor surgical clipping over endovascular intervention and vice versa. Finally, the authors propose a novel, simple, and clinically relevant algorithm for observation versus intervention in unruptured intracranial aneurysms based on the PHASES scoring system.